Presenting Statistical Results in Dissertations | APA Format
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Oliver Hastings
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Contents
- How to Present Statistical Results in a Dissertation
- T-Tests and Statistical Reporting
- ANOVA and Between-Group Comparisons
- Chi-Square Tests for Categorical Data
- Correlation Coefficients
- Multiple Regression
- Effect Sizes and Practical Significance
- Table Formatting in APA Style
- Common Reporting Mistakes to Avoid
- Why This Matters
Here's something worth knowing. The methodology chapter is one of the places where students most often lose marks unnecessarily, not because they've made bad methodological choices but because they haven't explained their choices clearly enough, haven't connected them to the epistemological assumptions underpinning their research design, or haven't demonstrated awareness of the limitations of their approach and how they've tried to address them. We'll help you avoid that.
How to Present Statistical Results in a Dissertation
Students hand in SPSS screenshots embedded in their dissertations. Tables look copy-pasted from software. Results sections read like machinery readouts. This's the gap in guidance that leaves your statistical work looking amateurish. APA format exists for a reason: it's clear, standardised, and allows readers to evaluate your analysis quickly. Learning to report correctly takes 20 minutes and improves your grade noticeably.
T-Tests and Statistical Reporting
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Never report a t-test as "t = 2.45, considerable." Report it properly. The format is t(df) = value, p = .xxx, d = value. For example: "There was a considerable difference between groups, t(58) = 2.78, p = .008, d = 0.72."
The degrees of freedom go in parentheses. The t value is the test statistic. The p value tells you the probability of this result if the null hypothesis were true. The d value is Cohen's d, the effect size. Effect size matters as much as statistical significance. A huge sample might yield p = .001 with d = 0.15 (statistically considerable but practically meaningless). A small sample might yield p = .06 with d = 0.80 (not quite considerable but a substantial effect).
Always report effect sizes. This's non-negotiable in modern reporting. Examiners notice when you omit them. If you haven't calculated effect sizes, your statistical training is incomplete.
ANOVA and Between-Group Comparisons
ANOVA format is F(df1, df2) = value, p = .xxx, η² = value. For example: "There was a considerable difference in test scores across the three conditions, F(2, 87) = 4.32, p = .017, η² = 0.09."
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The first df is degrees of freedom for the model (number of groups minus one). The second df is residual degrees of freedom (total N minus number of groups). The η² (eta-squared) is effect size. Cohen's benchmarks are η² = 0.01 (small), 0.06 (medium), 0.14 (large).
If ANOVA is considerable, report which groups differ using post-hoc tests. Don't just leave readers wondering which means are different. Say something like, "Follow-up paired comparisons using Bonferroni correction showed that Group A scored higher than Group B (p = .003) and Group C (p = .041), while Groups B and C didn't differ (p = .87)."
Chi-Square Tests for Categorical Data
Chi-square format is χ²(df, N = n) = value, p = .xxx, φ = value (or Cramér's V for larger tables). For example: "There was a considerable association between gender and subject choice, χ²(2, N = 214) = 6.14, p = .046, φ = 0.17."
The df is the degrees of freedom based on your contingency table dimensions. Always report N. The effect size is φ for 2x2 tables, Cramér's V for larger ones.
Correlation Coefficients
Report correlation as r(df) = value, p = .xxx. For example: "There was a strong positive correlation between study hours and exam performance, r(62) = 0.68, p < .001."
If you report multiple correlations, use a correlation matrix. Format it with variables listed in rows and columns, values below the diagonal, and significance indicators (typically asterisks: one asterisk for p < .05, two for p < .01, three for p < .001).
Multiple Regression
Regression reporting is more complex because you're reporting multiple predictors. Include the unstandardised coefficient (b), standardised coefficient (β), standard error (SE), t statistic, p value, and R² for the overall model. Example: "After controlling for socioeconomic status, years of experience predicted job satisfaction (β = 0.31, SE = 0.08, t(45) = 3.84, p < .001). The overall model explained 47% of variance in job satisfaction, R² = 0.47."
If you're reporting standardised coefficients only, that's acceptable, but unstandardised coefficients are preferable because they're interpretable (for every one-unit increase in X, Y changes by b units).
Effect Sizes and Practical Significance
Cohen's benchmarks for effect sizes differ by test. For Cohen's d: 0.2 (small), 0.5 (medium), 0.8 (large). For r: 0.1 (small), 0.3 (medium), 0.5 (large). For η² and R²: 0.01 (small), 0.06 (medium), 0.14 (large).
Statistical significance (p < .05) isn't the same as practical significance. A study with 10,000 participants might find a tiny effect is statistically considerable. A study with 50 participants might find a large effect that doesn't reach significance. Examiners want you to discuss both statistical and practical significance. Does the effect size matter in real terms? Is it large enough to be useful?
The quality of your dissertation conclusion will often determine the final impression your work makes on your marker, as it is the last thing they read before forming their overall assessment of your academic achievement. A strong conclusion does more than simply repeat the main points of your dissertation; it synthesises your findings in a way that demonstrates the overall contribution your research has made to knowledge in your field. You should also take the opportunity in your conclusion to reflect on what you would do differently if you were conducting the research again, as this kind of reflexivity demonstrates intellectual maturity and an honest assessment of your work. Ending with a clear statement of the implications of your research and the questions it leaves open for future investigation gives your dissertation a sense of intellectual momentum and leaves your reader with a positive final impression.
Table Formatting in APA Style
Table title goes above the table, numbered (Table 1, Table 2). Use plain text formatting: no shading, no excessive lines. Vertical lines are forbidden in APA style. Horizontal lines separate the header from the body and mark the table's end. Include a table note below with any necessary explanations, abbreviation definitions, and significance indicators.
That's what you're aiming for. The combination of careful argumentation, thorough source analysis, and consistent academic register is what separates dissertations that achieve distinction grades from those that merely pass, and it's a combination that takes deliberate practice and the right support to develop fully. You've got that support here.
Figures work similarly but titles go below the figure. Include a source note if the data is secondary. Don't embed SPSS output directly. Recreate tables and figures in proper format.
Common Reporting Mistakes to Avoid
Never report p values as "p = .000". SPSS displays this when p < .001. Report p < .001 instead. Actual p values can't be zero.
Don't report results sentences like "the result was considerable". Specific reporting is key. Include the test statistic, degrees of freedom, p value, and effect size.
Avoid rounding excessively. Report p values to two or three decimal places (p = .023, not p = .02). Means and standard deviations should match your data precision (if you measured to two decimal places, report two decimal places).
Why This Matters
Proper statistical reporting isn't cosmetic. It communicates clearly to readers. Examiners check that you understand not just how to run analyses but how to report them. Getting this right signals competence throughout your dissertation.
Frequently Asked Questions
Q: Can I report p values as p < .001 for anything less than .001? A: Yes. Most dissertations report p < .001 rather than exact values when p is very small. Some supervisors prefer exact values if they're still meaningful (like p = .0003). Ask your supervisor's preference.
Q: What if my analysis is exploratory and not hypothesis-driven? A: Report results exactly as you'd for hypothesis-driven analysis. Statistical reporting conventions don't change based on research design. You still report test statistics, p values, and effect sizes.
Q: Should I report results in the text and in tables, or just one? A: Generally both. In text, report the most important results with full statistics. In tables, present thorough results. This combination allows readers to find details while keeping the main narrative readable.
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